Methods and systems for controlling energy supply to different units, wherein each unit is connected to multiple utilities for receiving energy for operating its energy systems are known from prior art. Within such methods and systems a demand request signal is provided by at least one operational entity and/or by at least one utility for requesting a demand modification of a utility and/or of one form of energy. In this document, the terms utility and energy network are used interchangeably. A form of energy is electricity, gas or heat, for example.
Due to ongoing changes in energy systems that are driven by high penetration of renewable energy sources (RES) and other types of distributed energy resources (DER), many utility providers or utilities offer demand response (DR) programs as one possible means for energy management. Units in the form of end-users participating in a DR program agree to change their consumption compared to their normal usage in situations when resources are scarce because of low supply or high demand. The type of reward that a user decreasing its load will get from the utility provider is economical with details specified in DR contract.
Open automated demand response (OpenADR) is a standard developed to manage energy consumption via communication model designed for sending and receiving DR signals from utility providers to electric customers. It also specifies how third parties will interface to a Demand Response Automation Server (DRAS), that is used to facilitate the automation of customer response to various DR programs through communicating units.
DR programs are normally related to provision of electrical energy. However, not only electrical utilities need to request a temporal change in consumption. For instance, it can happen that a district heating utility cannot serve the entire demand, especially if its supply depends on uncontrollable RES. Thus, systems that consider DR for multiple utilities are emerging (see WO 2011/074925 A2 and U.S. Pat. No. 6,122,603).
WO 2011/074925 A2 shows participation of customers with multiple DR programs for different utilities. A prediction of customer's future demand per utility is based on CBL, Customer Baseline Load, calculation per utility. The customer's utility demand is predicted by establishing CBL.
U.S. Pat. No. 6,122,603 shows a multi-utility energy control system for monitoring consumption, cost of resource generation, plurality of utility types with a single master meter and for monitoring and controlling individual utility systems within a facility for determining possible utility cost adjustments to enhance cost effectiveness. The consumption rates are monitored and compared to theoretical and/or historical data to identify unexpected changes in consumption and to identify peak demands, surges and sags. Further, a software is disclosed for controlling utility consuming systems by adjusting actual utility consumption in response to predetermined parameters.
For use within the following disclosure of this document the term “cost function” is defined as follows: In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some “cost” associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (sometimes called a reward function or a utility function), in which case it is to be maximized. In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data (see Wikipedia http://en.wikipedia.org/). Note: costs can be real monetary costs, but also violations to performance KPIs.